Usage of contextual sarcasm has increased in the recent past, especially in online platforms. Texts found in microblogs, discussion forums, social media, etc., are plagued by grammatical inaccuracies and contain information which is highly temporal and contextual. In such scenarios, mining linguistic information becomes relatively inefficient and need arises for additional clues [1]}. wallace2014humans demonstrate this need by showing how traditional classifiers fail in instances where humans require additional context. They also indicate the importance of speaker and/or topical information associated to a text to gather such context. poria2016deeper use additional information by sentiment, emotional and personality representations of the input text. Previous works have mainly used historical posts of users to understand sarcastic tendencies [2]}, [3]}. khattri2015your try to find users' sentiments towards entities in their histories to find contrasting evidence. wallace2015sparse utilize sentiments and noun phrases used within a forum to gather context typical to that forum. Such forum based modeling simulates user-communities. Our work follows similar motivation where we explore context provided by user profiling and the topical knowledge embedded in the discourse of comments in discussion-forums (subreddits https://www.reddit.com/reddits/). amir2016modelling perform user modeling by learning embeddings that capture homophily. This work is closest to our approach given the fact that we too learn user embeddings to acquire context. However, we take a different approach that involve stylometric and personality description of the users. Empirical evidence shows that these proposed features are better than previous user modeling approaches. Moreover, we learn discourse features which has not been explored before in the context of this task.
